Low Latency Compressive Sensing using Multi-Resolution Analysis In Radar Signal Processing
Bibliographic record
Abstract
The digital world becomes dominated by electronic devices. The demand for higher speeds is growing every day which implies a higher demand for the signal acquisition and processing speeds and a wider signal bandwidth. The analog-to-digital converter (ADC) is the first step in the digital world. As stated by the Shannon/Nyquist sampling theorem, the ADC must sample the signal at a rate two times faster than the signal bandwidth in order to avoid data loss. However, the advances of ADC cannot always meet these demands. Therefore, considerable research has been done to find new approaches to acquiring the signal at a rate lower than the Nyquist rate (sub-Nyquist) while capturing the data with no losses or distortion. In recent years, compressive sensing (CS) has come to light as a new signal processing paradigm. CS exploits signal sparsity characteristics to acquire the signal using a number of samples much lower than the Nyquist rate. Our focus is studying CS and its applications to radar. We study different successful CS practical implementation approaches. However, these approaches lead to an undesired high-latency in the computation and signal reconstruction.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".